





"""simple docstring"""






from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 					__lowercase      (      nn.Module				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase = 16						,      _UpperCAmelCase = 88						,      _UpperCAmelCase = None						,      _UpperCAmelCase = 1						,      _UpperCAmelCase = 0.0						,      _UpperCAmelCase = 32						,      _UpperCAmelCase = None						,      _UpperCAmelCase = False						,      _UpperCAmelCase = None						,      _UpperCAmelCase = None						,      _UpperCAmelCase = "geglu"						,      _UpperCAmelCase = None						,      ):
									super().__init__()
									__a			:    str      							=	nn.ModuleList(
									    [
									        TransformeraDModel(
									            num_attention_heads=_UpperCAmelCase						,      attention_head_dim=_UpperCAmelCase						,      in_channels=_UpperCAmelCase						,      num_layers=_UpperCAmelCase						,      dropout=_UpperCAmelCase						,      norm_num_groups=_UpperCAmelCase						,      cross_attention_dim=_UpperCAmelCase						,      attention_bias=_UpperCAmelCase						,      sample_size=_UpperCAmelCase						,      num_vector_embeds=_UpperCAmelCase						,      activation_fn=_UpperCAmelCase						,      num_embeds_ada_norm=_UpperCAmelCase						,      )
									        for _ in range(2  )
									    ]  )

									# Variables that can be set by a pipeline:

									# The ratio of transformer1 to transformer2's output states to be combined during inference
									__a			:    Tuple      							=	0.5

									# The shape of `encoder_hidden_states` is expected to be
									# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
									__a			:    List[Any]      							=	[77, 257]

									# Which transformer to use to encode which condition.
									# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
									__a			:    Union[str, Any]      							=	[1, 0]







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase=None						,      _UpperCAmelCase=None						,      _UpperCAmelCase=None						,      _UpperCAmelCase = True						,      ):
									__a			:    Dict      							=	hidden_states

									__a			:    Any      							=	[]
									__a			:    Any      							=	0
									# attention_mask is not used yet
									for i in range(2  ):
													# for each of the two transformers, pass the corresponding condition tokens
													__a			:    Any      							=	encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
													__a			:    List[str]      							=	self.transformer_index_for_condition[i]
													__a			:    List[str]      							=	self.transformers[transformer_index](
													    _UpperCAmelCase						,      encoder_hidden_states=_UpperCAmelCase						,      timestep=_UpperCAmelCase						,      cross_attention_kwargs=_UpperCAmelCase						,      return_dict=_UpperCAmelCase						,      )[0]
													encoded_states.append(encoded_state - input_states  )
													tokens_start += self.condition_lengths[i]

									__a			:    Union[str, Any]      							=	encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
									__a			:    List[Any]      							=	output_states + input_states

									if not return_dict:
													return (output_states,)

									return TransformeraDModelOutput(sample=_UpperCAmelCase  )




from ...configuration_utils import PretrainedConfig
from ...utils import logging


a      :  List[Any]			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''timm_backbone'''

			def __init__(self,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=None,   **SCREAMING_SNAKE_CASE_,   )       ->   Tuple:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     backbone
						UpperCAmelCase_:		Optional[Any]							      =     num_channels
						UpperCAmelCase_:		Optional[Any]							      =     features_only
						UpperCAmelCase_:		Any							      =     use_pretrained_backbone
						UpperCAmelCase_:		List[str]							      =     True
						UpperCAmelCase_:		Union[str, Any]							      =     out_indices if out_indices is not None else (-1,)






